If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Members of the Google Brain team and Google AI this week open-sourced EfficientDet, an AI tool that achieves state-of-the-art object detection while using less compute. Creators of the system say it also achieves faster performance when used with CPUs or GPUs than other popular objection detection models like YOLO or AmoebaNet. When tasked with semantic segmentation, another task related to object detection, EfficientDet also achieves exceptional performance. Semantic segmentation experiments were conducted with the PASCAL visual object challenge data set. EfficientDet is the next-generation version of EfficientNet, a family of advanced object detection models made available last year for Coral boards.
The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.
The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade and thus it's a problem that needs now more than ever a lot of attention and a reliable solution that would reduce the negative impact it has over the modern society. This paper presents the design and early stage development of a novel counterfeit goods detection platform that makes use of the outstsanding learning capabilities of the classical VGG16 convolutional model trained through the process of "transfer learning" and a multi-stage fake detection procedure that proved to be not only reliable but also very robust in the experiments we have conducted so far using an image dataset of various goods which we gathered ourselves.
A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on Android phones or the Raspberry Pi. The guide is broken into three major portions. Each portion will have its own dedicated README file in this repository. This repository also contains Python code for running the newly converted TensorFlow Lite model to perform detection on images, videos, or webcam feeds.
Video filmed and edited for TV is typically created and viewed in landscape, but problematically, aspect ratios like 16:9 and 4:3 don't always fit the display being used for viewing. Fortunately, Google is on the case. Given a video and a target dimension, it analyzes the video content and develops optimal tracking and cropping strategies, after which it produces an output video with the same duration in the desired aspect ratio. As Google Research senior software engineer Nathan Frey and senior software engineer Zheng Sun note in a blog post, traditional approaches for reframing video usually involve static cropping, which often leads to unsatisfactory results. More bespoke approaches are superior, but they typically require video curators to manually identify salient content in each frame, track their transitions from frame to frame, and adjust crop regions accordingly throughout the video.
Despite significant accuracy improvement in convolutional neural networks (CNN) based object detectors, they often require prohibitive runtimes to process an image for real-time applications. State-of-the-art models often use very deep networks with a large number of floating point operations. Efforts such as model compression learn compact models with fewer number of parameters, but with much reduced accuracy. In this work, we propose a new framework to learn compact and fast ob- ject detection networks with improved accuracy using knowledge distillation  and hint learning . Although knowledge distillation has demonstrated excellent improvements for simpler classification setups, the complexity of detection poses new challenges in the form of regression, region proposals and less voluminous la- bels.
Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense . We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks. 1 Introduction Federated learning (FL) comes as a new distributed machine learning (ML) paradigm where multiple clients (e.g., mobile devices) collaboratively train an ML model without revealing their private data [ McMahan et al., 2017; Y ang et al., 2019b; Kairouz et al., 2019 ] . In a typical FL setting, a central server is used to maintain a global model and coordinate the clients. Each client transfers the local model updates to the central server for immediate aggregation, while keeping the raw data in their local storage.
This is the creator of gpt2-client here. Inspired by Hugging Face's Transformers, I built and launched Sightseer, a TensorFlow package that allows anyone to access SOTA Computer Vision and Object Detection models in 10 lines of code or less. In less than 10 lines of code, you can now access general-purpose SOTA models!!! For now, the Beta release supports YOLOv3 (Darknet by Joseph Redmon) and enables you to quickly load images and ingest them into the model. In the next release (coming very soon!), I'll be adding Facebook AI's Mask R-CNN model with support for video, webcam footage, and screen recordings and tools for data annotation and inter-format conversion (XML/CSV/JSON/TFRecords).
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between W eBankand Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.